Samaah Koshy

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When it comes to credit scores, credit history and credit reports, you may have heard terms like "debt-to-income ratio” and “debt-to-credit ratio.” But what do these terms mean, and more importantly, how are they different?

Your debt-to-income ratio (DTI) refers to the total amount of debt payments you owe every month divided by the total amount of money you earn each month. A DTI ratio is usually expressed as a percentage.

This ratio includes all of your total recurring monthly debt — credit card balances, rent or mortgage payments, vehicle loans and more.

To calculate your DTI ratio, divide your total recurring monthly debt by your gross monthly income — the total amount you earn each month before taxes, withholdings and expenses.

For example, if you owe $2,000 in debt each month and your monthly gross income is $6,000, your DTI ratio would be 33 percent. In other words, you spend 33 percent of your monthly income on your debt payments.

Lenders may consider your DTI ratio as one factor when determining whether to lend you additional money and at what interest rate. Generally speaking, the lower a DTI ratio you have, the less risky you appear to lenders. The preferred maximum DTI ratio varies. However, for most lenders, 43 percent is the maximum DTI ratio a borrower can have and still be approved for a mortgage.

If you have a high DTI ratio, you're probably putting a large chunk of your monthly income toward debt payments. Lowering your DTI ratio can help you shift your focus to building wealth for the future.

Here are a few steps you can take to help lower your DTI ratio:

Your debt-to-credit ratio, also known as your credit utilization rate or debt-to-credit rate, represents the amount of revolving credit you're using divided by the total amount of credit available to you.

Revolving credit accounts include things like credit cards and lines of credit. They don't require a fixed payment each month, and you can re-use the credit as you pay your balance down. On the other hand, installment loans are things like a mortgage or a vehicle loan, with a fixed payment each month. When installment loans are paid, the account is closed. Installment loans generally are not included in your debt-to-credit ratio.

You can determine your debt-to-credit ratio by dividing the total amount of credit available to you, across all your revolving accounts, by the total amount of debt on those accounts.

For example, say you have two credit cards with a combined credit limit of $10,000. If you owe $4,000 on one card and $1,000 on the other for a combined total of $5,000, your debt-to-credit ratio is 50 percent.

Many lenders use credit scoring formulas that take your debt-to-credit ratio into consideration. In general, lenders like to see a debt-to-credit ratio of 30 percent or lower. If your ratio is higher, it could signal to lenders that you're a riskier borrower who may have trouble paying back a loan. As a result, your credit score may suffer.

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In the first part, I'll show you how to install the ZBar library (with Python bindings). The ZBar library will be used together with OpenCV to scan and decode barcodes and QR codes. Once ZBar and OpenCV are properly configured I'll demonstrate how to scan barcodes and QR codes in a single image.

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The advantages of a string assertion are less overhead when assertion checking is off and messages containing the assertion expression when an assertion fails. This means that if you pass a boolean condition as assertion this condition will not show up as parameter to the assertion function which you may have defined with the assert_options() function, the condition is converted to a string before calling that handler function, and the boolean FALSE is converted as the empty string.

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• Prepare 800 mL of distilled water in a suitable container.
• Add 150 g of DTT (DL-dithiothreitol, anhydrous m.w. = 154.25) to the solution.
• Dispense into 1-mL aliquots, and store them in the dark (wrapped in aluminum foil) at -20°C (indefinitely). Do not autoclave DTT or solutions containing it.

Like NXC said, Python variable names actually point to an object, and not a specific spot in memory.

newList = oldList would create two different variables that point to the same object, therefore, changing oldList would also change newList.

However, when you do newList = oldList[:], it "slices" the list, and creates a new list. The default values for [:] are 0 and the end of the list, so it copies everything. Therefore, it creates a new list with all the data contained in the first one, but both can be altered without changing the other.

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